Distributed Intelligent System Architecture for UAV-Assisted Monitoring of Wind Energy Infrastructure
This provides a scalable and reliable solution for wind turbine maintenance, addressing efficiency and reliability issues in renewable energy infrastructure, though it is incremental as it builds on existing UAV and sensor technologies.
The paper tackles the problem of detecting defects in wind turbines by proposing a distributed intelligent system architecture using UAVs with visual and thermal sensors, resulting in up to 94% accuracy in defect detection and reducing inspection time to 1.5 hours per turbine.
With the rapid development of green energy, the efficiency and reliability of wind turbines are key to sustainable renewable energy production. For that reason, this paper presents a novel intelligent system architecture designed for the dynamic collection and real-time processing of visual data to detect defects in wind turbines. The system employs advanced algorithms within a distributed framework to enhance inspection accuracy and efficiency using unmanned aerial vehicles (UAVs) with integrated visual and thermal sensors. An experimental study conducted at the "Staryi Sambir-1" wind power plant in Ukraine demonstrates the system's effectiveness, showing a significant improvement in defect detection accuracy (up to 94%) and a reduction in inspection time per turbine (down to 1.5 hours) compared to traditional methods. The results show that the proposed intelligent system architecture provides a scalable and reliable solution for wind turbine maintenance, contributing to the durability and performance of renewable energy infrastructure.